SafeDriveVision is a computer vision project aimed at enhancing road safety. This project leverages deep learning models to detect and alert in real-time the dangerous behaviors of drivers, such as using a phone while driving or showing signs of drowsiness. The primary goal is to help reduce road accidents by alerting drivers to their potentially hazardous actions.
To read a detailed explanation of the SafeDriveVision project, check out the article on Medium: SafeDriveVision: Enhancing Road Safety through Computer Vision
- Phone Use Detection: Utilizes the YOLOv5 model to identify drivers using their phones during driving.
- Drowsiness Detection: Incorporates a custom detector (sharp detector) to monitor signs of driver fatigue.
- Real-Time Alerts: Implements an alert system that warns the driver when a risky behavior is detected.
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PyTorch >= 0.4.1 (PyTorch v1.1.0 is tested successfully.)
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Python >= 3.6 (Numpy, Scipy, Matplotlib)
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Dlib
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OpenCV (Python version, for image IO operations.)
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Cython (For accelerating depth and PNCC render.)
- Clone the repository: :
git clone https://github.com/Boubker10/SafeDriveVision
- Install the necessary dependencies: :
pip install -r requirements.txt
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Download the YOLOv5 checkpoint file (
yolov5m.pt):Before running the project, make sure to download the YOLOv5 model checkpoint file
yolov5m.ptfrom the official repository. You can download it here.
- 2d_sparse
python SafeDriveVision.py --onnx
2ds.mp4
- 3d Face Reconstruction
python SafeDriveVision.py --onnx --opt 3d
3d.mp4
- 2d V0
python SafeDriveVisionV0.py
v0.mp4
- Google Colab
If you want to use the notebook version on Google Colab, download the file SafeDriveVision.ipynb and add it to your workspace
FaceBoxesNet is a neural network architecture designed for object detection. Below is the detailed description of its layers:
Phase: Can be either 'train' or 'test'.
Conv1: CRelu with 3 input channels, 24 output channels, kernel size 7x7, stride 4, padding 3.
Pooling: Max pooling with kernel size 3x3, stride 2, padding 1.
Conv2: CRelu with 48 input channels, 64 output channels, kernel size 5x5, stride 2, padding 2.
Pooling: Max pooling with kernel size 3x3, stride 2, padding 1.
Inception: Three consecutive Inception blocks.
Detection Source 1: Output after the Inception blocks.
Conv3_1: BasicConv2d with 128 input channels, 128 output channels, kernel size 1x1.
Conv3_2: BasicConv2d with 128 input channels, 256 output channels, kernel size 3x3, stride 2, padding 1.
Detection Source 2: Output after Conv3_2.
Conv4_1: BasicConv2d with 256 input channels, 128 output channels, kernel size 1x1.
Conv4_2: BasicConv2d with 128 input channels, 256 output channels, kernel size 3x3, stride 2, padding 1.
Detection Source 3: Output after Conv4_2.
Multibox Layers: Localization and classification layers applied to the detection sources.
Branch1x1: BasicConv2d with kernel size 1x1.
Branch1x1_2: BasicConv2d with kernel size 1x1 after pooling.
Branch3x3_reduce: BasicConv2d with kernel size 1x1.
Branch3x3: BasicConv2d with kernel size 3x3.
Branch3x3_reduce_2: BasicConv2d with kernel size 1x1.
Branch3x3_2: BasicConv2d with kernel size 3x3.
Branch3x3_3: BasicConv2d with kernel size 3x3.
2D Convolution followed by BatchNorm and ReLU.
2D Convolution followed by BatchNorm and concatenation of the output with its negative, then ReLU.
The project resource can be found at 3DDFA.
We welcome contributions to the SafeDriveVision project! If you have suggestions for improvements or notice any issues, here's how you can contribute:
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Fork the Repository: Start by forking the repository. This creates a copy of the project in your own GitHub account.
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Clone Your Fork: Clone the forked repository to your local machine. This allows you to work on the files locally.
git clone https://github.com/Boubker10/SafeDriveVision.git cd SafeDriveVision -
Create your Feature Branch (git checkout -b feature/AmazingFeature)
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Commit your Changes
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git commit -m 'Add some AmazingFeature' -
Push to the Branch
git push origin feature/AmazingFeature -
Open a Pull Request
- Boubker - Boubker10
Boubker BENNANI
LinkedIn: @boubkerbennani
Email: boubkerbennani44@gmail.com
Project Link: https://github.com/Boubker10/SafeDriveVision
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